{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "db8acd45-429f-41a1-9356-549ff0685c6a",
   "metadata": {},
   "source": [
    "This notebook is prepared for the analysis of $g^2$ data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e5bb8a-89e6-4e44-adf3-e94bd95c76f2",
   "metadata": {},
   "source": [
    "# Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6468b181-c9ae-421c-99f1-828bcb976ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib.ticker import FormatStrFormatter,MaxNLocator\n",
    "from mpl_toolkits.axes_grid.inset_locator import inset_axes, InsetPosition, mark_inset\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import time as time\n",
    "import datetime\n",
    "import scipy.optimize as sp\n",
    "import scipy\n",
    "import shutil\n",
    "import lmfit\n",
    "import os\n",
    "    \n",
    "\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "import h5py\n",
    "\n",
    "from scipy.stats import norm,poisson\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.signal import find_peaks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17777c04-2a3e-49fb-bc40-e43561207003",
   "metadata": {},
   "source": [
    "# Useful functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10fdd904-d9fc-445a-977a-5b228987a321",
   "metadata": {},
   "outputs": [],
   "source": [
    "def counts_in_window(click_tot,time0,time1):\n",
    "    counter_list=[]\n",
    "    for click_list in click_tot:\n",
    "        counter=0\n",
    "        for click in click_list:\n",
    "            if click<time1 and click>time0:\n",
    "                counter+=1\n",
    "        counter_list.append(counter)        \n",
    "    counter_list=np.array(counter_list)   \n",
    "    return counter_list\n",
    "\n",
    "def heralded_counts(click_tot,time0,time1):\n",
    "    click_herald=[]\n",
    "    time_herald=[]\n",
    "    for click_list in click_tot:\n",
    "        counter=0\n",
    "        herald_list=[]\n",
    "        for click in click_list:\n",
    "            if click<time1 and click>time0 and counter==0:\n",
    "                time_herald.append(click)\n",
    "                counter+=1\n",
    "            elif counter>0:\n",
    "                counter+=1\n",
    "                herald_list.append(click)#-click_herald)\n",
    "        if counter>0:\n",
    "            click_herald.append(herald_list)#-time_herald)\n",
    "    click_herald=np.array(click_herald)\n",
    "    return click_herald,time_herald\n",
    "\n",
    "def exp_decay(t,T,a,b):\n",
    "    return np.exp(-t/T)*a+b\n",
    "\n",
    "def compute_g2(click_tot,time0,time1,bins,title, plot_fit=False):\n",
    "\n",
    "    herald_list, _ = heralded_counts(click_tot,time0,time1)\n",
    "\n",
    "    bin_size=(bins[1:]-bins[:-1]).mean(0)\n",
    "    bin_center=(bins[1:]+bins[:-1])/2\n",
    "\n",
    "    plt.figure(figsize=(5,5))\n",
    "\n",
    "    hist,bins=np.histogram(np.concatenate(click_tot),bins=bins)\n",
    "    n_tot=hist/len(click_tot)/bin_size\n",
    "    plt.bar(bin_center,n_tot,width=bin_size, label='no heralding')#, color='blue')\n",
    "\n",
    "\n",
    "    guess=[1,0.07,0.1]\n",
    "    popt,_=sp.curve_fit(exp_decay,bin_center,n_tot,guess)\n",
    "\n",
    "    hist_herald,bins=np.histogram(np.concatenate(herald_list),bins=bins)\n",
    "    h_tot=hist_herald/len(herald_list)/bin_size\n",
    "    if plot_fit:\n",
    "        plt.plot(bin_center,exp_decay(bin_center,*popt))\n",
    "\n",
    "    plt.bar(bin_center,h_tot,width=bin_size,alpha=0.7, label='heralding on a click before %.2f ms'%time1)#,color='orange')\n",
    "    plt.ylabel('click rate (1/ms)')\n",
    "    plt.xlabel('time (ms)')\n",
    "    plt.legend(loc='lower right')\n",
    "    #plt.xlim(0,10)\n",
    "    plt.title(title)\n",
    "    \n",
    "    g2=h_tot/n_tot\n",
    "\n",
    "\n",
    "\n",
    "#    plt.figure(figsize=(7,7))\n",
    "#    plt.plot(bin_center,g2,'-o')\n",
    "#    plt.ylabel('g2(tau)')\n",
    "#    plt.xlabel('tau (ms)')\n",
    "\n",
    "    return g2, h_tot, n_tot, popt\n",
    "\n",
    "\n",
    "def compute_g2_noplot(click_tot,time0,time1,bins,title, plot_fit=False):\n",
    "\n",
    "    herald_list, _ = heralded_counts(click_tot,time0,time1)\n",
    "\n",
    "    bin_size=(bins[1:]-bins[:-1]).mean(0)\n",
    "    bin_center=(bins[1:]+bins[:-1])/2\n",
    "\n",
    "\n",
    "    hist,bins=np.histogram(np.concatenate(click_tot),bins=bins)\n",
    "    n_tot=hist/len(click_tot)/bin_size\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "    hist_herald,bins=np.histogram(np.concatenate(herald_list),bins=bins)\n",
    "    h_tot=hist_herald/len(herald_list)/bin_size\n",
    "    \n",
    "    \n",
    "    g2=h_tot/n_tot\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    return g2, h_tot, n_tot\n",
    "  \n",
    "def compute_g2_keep1stBar(click_tot,time0,time1,dead_time, title, plot_fit=False):\n",
    "\n",
    "    herald_list, _ = heralded_counts(click_tot,time0,time1)\n",
    "\n",
    "    bins_n=np.arange(time0,7.6,time1-time0)\n",
    "    bins_h=np.arange(time1,7.6,time1-time0)\n",
    "    bin_size=(bins_n[1:]-bins_n[:-1]).mean(0)\n",
    "    bin_center_n=(bins_n[1:]+bins_n[:-1])/2\n",
    "    bin_center_h=(bins_h[1:]+bins_h[:-1])/2\n",
    "    \n",
    "\n",
    "    plt.figure(figsize=(5,5))\n",
    "\n",
    "    hist,bins=np.histogram(np.concatenate(click_tot),bins=bins_n)\n",
    "    n_tot=hist/len(click_tot)/bin_size\n",
    "    plt.bar(bin_center_n+dead_time,n_tot,width=bin_size, label='no heralding')#, color='blue')\n",
    "\n",
    "\n",
    "    guess=[1,0.07,0.1]\n",
    "    popt,_=sp.curve_fit(exp_decay,bin_center_n,n_tot,guess)\n",
    "\n",
    "    hist_herald,bins=np.histogram(np.concatenate(herald_list),bins=bins_h)\n",
    "    h_tot=hist_herald/len(herald_list)/bin_size\n",
    "    if plot_fit:\n",
    "        plt.plot(bin_center_n,exp_decay(bin_center_n,*popt))\n",
    "\n",
    "    plt.bar(bin_center_h+dead_time,h_tot,width=bin_size,alpha=0.7, label='heralding on a click before %.2f ms'%time1)#,color='orange')\n",
    "    plt.ylabel('click rate (1/ms)')\n",
    "    plt.xlabel('time (ms)')\n",
    "    plt.legend(loc='lower right')\n",
    "    #plt.xlim(0,10)\n",
    "    plt.title(title)\n",
    "    \n",
    "    g2=h_tot/n_tot[1:]\n",
    "\n",
    "\n",
    "    return g2, h_tot, n_tot, popt, bin_center_n, bin_center_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac117d06-98fc-4e97-aec3-c858bc116039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dead_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d06e591-c053-4dd8-b8a1-6bf8924dcce2",
   "metadata": {},
   "source": [
    "# dataloading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ae1ea05-85cc-4e2a-8497-c415c66ba036",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "measurement time: 25.7 hours\n",
      "darkcount rate = 0.106\n"
     ]
    }
   ],
   "source": [
    "with h5py.File(r'.\\g2.hdf5', 'r') as f:\n",
    "    f.keys()\n",
    "    click_time=f['click_time_array'][()]\n",
    "    pulse_time=f['pulse_time_array'][()]\n",
    "print('measurement time: %.1f hours'%(pulse_time[-1]*1e-3/3600))\n",
    "\n",
    "click_list=[]\n",
    "j=0\n",
    "dead_time=0.06\n",
    "for i in range(len(pulse_time)-1):\n",
    "    click_=[]\n",
    "    while j<len(click_time) and click_time[j]<pulse_time[i+1]:\n",
    "        click_.append((click_time[j]-pulse_time[i]+np.abs((np.random.randn()*0.01))-dead_time))\n",
    "        j+=1\n",
    "    click_list.append(click_)\n",
    "click_list=np.array(click_list,dtype=object)\n",
    "\n",
    "click_0=click_list[::2]\n",
    "click_pi=click_list[1::2]\n",
    "N_rep=len(pulse_time)/2\n",
    "\n",
    "darkcountrate=len(np.concatenate(click_0))/N_rep/max(np.concatenate(click_0))\n",
    "print(\"darkcount rate = %.3f\"%darkcountrate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ef3782-5ba7-4949-b2a6-e968967c6fcb",
   "metadata": {},
   "source": [
    "# $g^2(k)$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b06782e-66aa-460a-88c4-c8c4e528b966",
   "metadata": {},
   "source": [
    "### g2(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "557b61d0-d3a0-4923-8755-951b26fd493e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate counts in a chosen window\n",
    "time0=0.1\n",
    "time1=0.45\n",
    "time2=0.8\n",
    "\n",
    "## Number of clicks in a chosen window for each pulse sequence\n",
    "counter_array0=(counts_in_window(click_pi,time0,time1))\n",
    "counter_array1=(counts_in_window(click_pi,time1,time2))\n",
    "counter_arrayDC=(counts_in_window(click_0,time0,time2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "547e3bf2-cf64-42ab-bad5-10e6306a942e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6669736918354654, 0.487425424685848]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24fab97e340>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Calculate time window and average numbers\n",
    "t_window0=time1-time0\n",
    "t_window1=time2-time1\n",
    "t_windowDC=time2-time0\n",
    "N_avg0=len(counter_array0)\n",
    "N_avg1=len(counter_array1)\n",
    "N_avgDC=len(counter_arrayDC)\n",
    "\n",
    "## Averaged click rate in a given window\n",
    "n_pi_0=counter_array0.sum()/N_avg0/t_window0\n",
    "n_pi_1=counter_array1.sum()/N_avg1/t_window1\n",
    "n_0=counter_arrayDC.sum()/N_avgDC/t_windowDC\n",
    "\n",
    "SNR=[(n_pi-n_0)/n_0 for n_pi in [n_pi_0, n_pi_1]]\n",
    "#print(SNR)\n",
    "\n",
    "Ntot=20\n",
    "pulse_index=np.arange(-Ntot,Ntot+1)\n",
    "g2=[]\n",
    "for N in pulse_index:\n",
    "    coincidence=0.5*(counter_array0[Ntot+N:-Ntot-1]*counter_array1[Ntot:-N-Ntot-1]+counter_array1[Ntot+N:-Ntot-1]*counter_array0[Ntot:-N-Ntot-1]).mean()\n",
    "    norm1=(counter_array0[Ntot+N:-Ntot-1]).mean()\n",
    "    norm2=(counter_array1[Ntot:-N-Ntot-1]).mean()\n",
    "    g2.append(coincidence/(norm1*norm2))\n",
    "g2=np.array(g2)\n",
    "\n",
    "\n",
    "A0=SNR[0]#~0.665\n",
    "A1=SNR[1]#~0.488\n",
    "g2_i=(A0+A1+1)/((A0+1)*(A1+1))#(2*A+1)/(A+1)**2\n",
    "g2_corr=((1+A0)*(1+A1)/(A0*A1))*(g2-g2_i)\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(pulse_index,g2,alpha=0.7, label=\"measured g2\")\n",
    "plt.bar(pulse_index,g2_corr, alpha=0.7, label=\"corrected g2\")\n",
    "\n",
    "plt.ylabel('g2')\n",
    "plt.xlabel('pulse offset $k$')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830175c5-e696-4761-88d2-c7f95be73979",
   "metadata": {},
   "source": [
    "### g2(k) with error bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e88518c-62a2-41af-880a-e97fbe65a710",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['g2', 'g2_corr', 'g2_corr_error', 'g2_error', 'pulse_index']>\n"
     ]
    }
   ],
   "source": [
    "with h5py.File(r'.\\g2_k_bootstrap.hdf5', 'r') as f:\n",
    "    print(f.keys())\n",
    "    g2_corr_error=f['g2_corr_error'][()]\n",
    "    g2_error=f['g2_error'][()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86a86895-8dce-4354-84c6-81aa934da23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x24fadc03e80>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Beautiful plot\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(pulse_index,g2_corr)\n",
    "plt.ylabel('Corrected $g^{(2)}$')\n",
    "plt.xlabel('Offset between excitation pulses $k$')\n",
    "plt.errorbar(pulse_index, g2_corr, yerr=g2_corr_error, fmt='None', color=\"r\")\n",
    "plt.axhline(1)\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(pulse_index,g2)\n",
    "plt.ylabel('Uncorrected $g^{(2)}$')\n",
    "plt.xlabel('Offset between excitation pulses $k$')\n",
    "plt.errorbar(pulse_index, g2, yerr=g2_error, fmt='None', color=\"r\")\n",
    "plt.axhline(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a878b98d-ebf5-4ed5-9412-1aa2b4d459b5",
   "metadata": {},
   "source": [
    "### Bootstrap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7955bd4-f8b4-4638-8d05-1d4dc574e426",
   "metadata": {},
   "source": [
    "Bootstrap to get error bars from the statistics\n",
    "\n",
    "If the file \"g2_k_bootstrap.hdf5\" exists, we can skip this step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7942ac12-729e-46e3-8f81-83107e4fc0bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
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      "2\n",
      "0.321103792423831\n",
      "3\n",
      "0.2504541207126872\n",
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      "0.25629383508125136\n"
     ]
    }
   ],
   "source": [
    "time0=0.1\n",
    "time1=0.45\n",
    "time2=0.8\n",
    "t_window0=time1-time0\n",
    "t_window1=time2-time1\n",
    "t_windowDC=time2-time0\n",
    "\n",
    "counter_arrayDC=(counts_in_window(click_0,time0,time2))\n",
    "N_avgDC=len(counter_arrayDC)\n",
    "n_0=counter_arrayDC.sum()/N_avgDC/t_windowDC\n",
    "\n",
    "g2_list=[]\n",
    "g2corrected_list=[]\n",
    "\n",
    "for i in range(100):\n",
    "    click_pi_bootstrap=click_pi[np.random.randint(0,len(click_pi),len(click_pi))]\n",
    "\n",
    "    ## Number of clicks in a chosen window for each pulse sequence\n",
    "    counter_array0=(counts_in_window(click_pi_bootstrap,time0,time1))\n",
    "    counter_array1=(counts_in_window(click_pi_bootstrap,time1,time2))\n",
    "\n",
    "    ## Calculate time window and average numbers\n",
    "    N_avg0=len(counter_array0)\n",
    "    N_avg1=len(counter_array1)\n",
    "\n",
    "    ## Averaged click rate in a given window\n",
    "    n_pi_0=counter_array0.sum()/N_avg0/t_window0\n",
    "    n_pi_1=counter_array1.sum()/N_avg1/t_window1\n",
    "    \n",
    "\n",
    "    SNR=[(n_pi-n_0)/n_0 for n_pi in [n_pi_0, n_pi_1]]\n",
    "\n",
    "    Ntot=20\n",
    "    pulse_index=np.arange(-Ntot,Ntot+1)\n",
    "    \n",
    "    g2=[]\n",
    "    for N in pulse_index:\n",
    "        coincidence=0.5*(counter_array0[Ntot+N:-Ntot-1]*counter_array1[Ntot:-N-Ntot-1]+counter_array1[Ntot+N:-Ntot-1]*counter_array0[Ntot:-N-Ntot-1]).mean()\n",
    "        norm1=(counter_array0[Ntot+N:-Ntot-1]).mean()\n",
    "        norm2=(counter_array1[Ntot:-N-Ntot-1]).mean()\n",
    "        g2.append(coincidence/(norm1*norm2))\n",
    "    g2=np.array(g2)\n",
    "    \n",
    "    A0=SNR[0]#0.665\n",
    "    A1=SNR[1]#0.488\n",
    "    g2_i=(A0+A1+1)/((A0+1)*(A1+1))#(2*A+1)/(A+1)**2\n",
    "    g2_corr=((1+A0)*(1+A1)/(A0*A1))*(g2-g2_i)\n",
    "    \n",
    "    g2_list.append(g2)\n",
    "    g2corrected_list.append(g2_corr)\n",
    "    print(i)\n",
    "    print(g2_corr[Ntot])\n",
    "    \n",
    "g2corrected_list=np.array(g2corrected_list)\n",
    "g2_list = np.array(g2_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b35959-20ae-45b5-9f4c-3ee86ee1b4a7",
   "metadata": {},
   "source": [
    "# $g^2(\\tau$)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b75a46e-6204-49c1-ac50-79bc32300a38",
   "metadata": {},
   "source": [
    "## Heralded counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38d7f5b5-3859-4d14-9ec4-f8cbf11f1446",
   "metadata": {},
   "source": [
    "Calculate counts heralded on the first bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c2dcf6a3-9384-436a-a522-bc88e5189665",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zw267001\\AppData\\Local\\Temp\\ipykernel_12564\\2631037232.py:27: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  click_herald=np.array(click_herald)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "137c679323f843f9991f7205c87b4f48",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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0An4CKgGXA08kuygREcl9mh3uPt/M0tw9k8g8zVNSUJeISJkXG9CbgkmLvjazB4h8cKgJ9EVEUiB2iOOiYNnVRCbPbwCcleyiREQk6gg6uPnrve5+IbAFuCtlVYmIyJ9H0MGYc+1giENERFIsdgx6ITA5uJpwY9ZCdx+czKJERCR3QC8NvsoRucxbRERSJEdAu7vGnUVEQiIRd/UWEZFioIAWEQmp2Lk4asU2MLNGyStHRESyxB5BjzGzallPzKwZuiehiEhKxAb0fURCei8zO4rITWMvTH5ZIiISexbHu2a2BzCByGl2Pdz9p5RUJiJSxmXd8uoJwKOWVwMWANeYGe5+bSqKExEpy7KOoGfELP8q2YWIiEhOWbe8ehnAzKoAW4J5ObImUNozdeWJiJRdsR8SfkTkTipZKgEfJq8cERHJEhvQFd19Q9aT4HHl5JYkIiKQO6A3mlnLrCfBqXabk1uSiIhA7tnsrgfeMrOlwfO6wF+TWpGIiAC5z4OebmaHAAcDBnzv7ttTUpmISBmXdR50e3f/2Mx6xqxvHJwHPSIFtYmIlGlZR9AnAh8DZ8Rp44ACWkQkybLOgx4Y/HtpassREZEsWUMcN+yqke5JKCKSfFlDHLr/oIhIyGQNcehehCIiIRN7R5WXzaxG1POaZvZCfp2YWScz+8HM5ptZ/zjrzcweD9bPjr4YJlifZmazzGxsEV6LiEipEnslYXN3X5v1xN3XAC121UEwodJTQGegGdAruBNLtM5A4+CrD/BMzPrrgHmFLV5EpDSLDehyZlYz60lwj8LYqw1jHQPMd/cF7r4NGAZ0j2nTHXjFI6YCNcysbrCPdOB04PkivA4RkVInNnwfBqaY2XAi5z+fC9ybTx/1gSVRzzOA1gVoUx9YBjwK3II+qBQRySHHEbS7vwKcBfwOrAB6uvur+fRhcZZ5QdqYWVdgubvne4MAM+tjZjPMbMaKFSvyay4iUuLlGr5w9++A7wrRRwbQIOp5OrC0gG3OBrqZWRegIlDNzP7r7rluVOvuzwLPArRq1Sr2F4CISKkTOwa9O6YTmbOjkZlVAM4DRse0GQ1cHJzN0QZY5+7L3P1Wd09394bBdh/HC2cRkbIovw8A8+XuO8zsamA8kAa84O5zzezKYP0QYBzQBZgPbAJ0SbmISD6KHNAA7j6OSAhHLxsS9diBvvn08QnwSSLqEREpDRIxxCEiIsVAAS0iElIKaBGRkFJAi4iElAJaRCSkFNAiIiGlgBYRCSkFtIhISCmgRURCSgEtIhJSCmgRkZBSQIuIhJQCWkQkpBTQIiIhpYAWEQkpBbSISEgpoEVEQkoBLSISUgpoEZGQUkCLiISUAlpEJKQU0CIiIaWAFhEJKQW0iEhIKaBFREJKAS0iElIKaBGRkFJAi4iElAJaRCSkFNAiIiGlgBYRCSkFtIhISCmgRURCSgEtIhJSCmgRkZBSQIuIhJQCWkQkpBTQIiIhpYAWEQmphAS0mXUysx/MbL6Z9Y+z3szs8WD9bDNrGSxvYGYTzWyemc01s+sSUY+ISGlQ5IA2szTgKaAz0AzoZWbNYpp1BhoHX32AZ4LlO4Ab3b0p0AboG2dbEZEyKRFH0McA8919gbtvA4YB3WPadAde8YipQA0zq+vuy9x9JoC7/wHMA+onoCYRkRIvEQFdH1gS9TyD3CGbbxszawi0AKYloCYRkRIvEQFtcZZ5YdqY2V7A28D17r4+7k7M+pjZDDObsWLFit0uVkSkpEhEQGcADaKepwNLC9rGzPYgEs6vufuIvHbi7s+6eyt3b1W7du0ElC0iEm6JCOjpQGMza2RmFYDzgNExbUYDFwdnc7QB1rn7MjMzYCgwz90HJ6AWEZFSo3xRO3D3HWZ2NTAeSANecPe5ZnZlsH4IMA7oAswHNgGXBpu3Ay4C5pjZ18GyAe4+rqh1iYiUdEUOaIAgUMfFLBsS9diBvnG2m0T88WkRkTJPVxKKiISUAlpEJKQU0CIiIaWAFhEJqYR8SCjFzyrsRdWje1G+Wl0iZyeKyK7Mmzcvz3UVK1YkPT2dPfbYI4kVFZ4CuoSoenQv6hxwGNUr76mAFimAavUPjrvc3Vm1ahUZGRk0atQoyVUVjoY4Sojy1eoqnEUSwMzYe++92bJlS6pLyZcCuoQwM4WzSIKUlP9LCmhJiqv69WfU2PeLpe/Tz76Imd/MybX8tTdHcNNtdwMw9NVhvDF8VLHsX6S4aAy6hGr+xPyE9jf7moMS2l8iuTvuTrlyu3888beLzktgRSLJoSNoKZBFSzI4+qQuXHPz7bRu35Ue51/G5s2RMbzZc+dxyhl/5dgO3bjgb1ezZu26uH1MmTaDU7ufR/NjO+Q4mn7smaGcdPrZHNuhG/c99HiO/d0w4C6O79STjKXL6HfrnZzY5Sxat++a3S7Wf//3Ni2PP40uZ13I1Omzspff//ATPD5kKBA54r7j3oc4+fRzaHn8aUyZNgOATZs3c8mV13Nsh270vqof7bueG/fIXCRZFNBSYD//sogrep/PtI/HUr1aNUaPmwDAldf9g7sG3MiUD0fT7JAm/PuRp+Ju/9vyFYwf+TpvvjSEO++PTF740aeT+PmXhUwc+xaTJozi6zlzmTx1OgA//fwLvc7qzqTxI9kvvT6333I9n457mykfvMPkqdP59rsfcvb/+3Luf/hJJox6g1FvvMAPP+X9V0Zm5g4mvvsWg+4cwKCg3udffp0a1asx5cPR3HLdVXw9Z26R3zORolBAS4Ht3yCd5oc2BeDIww9lccavrFv/B+vW/8FxbY8BoNc5PZgcHJHG6npaB8qVK8chTQ5ixcqVAHz82WQmfjaZ4087kxM69eTH+b/w8y+LAGiQXo+jjzoye/uRY9/n+E49Of60M5n343y+jwngGbNmc1zbY9hn71pUqFCBnmd0zvO1nNG5Y+R1ND+UxUt+BWDq9Jmc1a0LAM0OacKhTeOfpiWSLBqDlgLbc88K2Y/T0sqxZcuOQm1focKfFwW4//mg39V9uOzCnGPEi5ZkUKVy5eznCxdn8MSQF5j47lvUrFGdq/r1Z+vWrbn2UdAP57NqSUtLIzNzR1BK7I2ARFJLR9BSJNWrVY0MCwRHzf97+x2Oa3N0gbdvf+Jx/HfYCDZs3AjA0mW/s2Llqlzt/tiwgSqVK1G9WlWWr1jJBxM/z9WmVYvmTPpiOqvXrGH79u2Mend8oV5Lm6OPYmQwNv79j/P57vsfC7W9SKLpCFqK7JlHB9Gv/51s3ryZhvs34KmH7yvwtqeceBw//rSAU7tFjqCrVKnMs48/SFpazmOHw5sdQvPDmtK6fVca7teA1q1a5uqrzl/2pf8NfenQ7Tzq7FubIw5rRmZmZoFrufySXlx5fX+O7dCN5oc15dCmTahetWqBtxdJNCuJf9a1atXKZ8yIP86Zl4b93y1U++f3eLBQ7Yvb3l0GcuB+9VJdRqmWmZnJ9u07qFhxTxYsXEz383rz1WfvU6FChfw3ltDJ61LvLPPmzaNp06Y5lpnZV+7eqjjrKgwdQYsENm3eTNdzL2HH9h24O4Pvu1PhLCmlgBYJVN1rLz4d93aqyxDJpg8JRURCSgEtIhJSCmgRkZBSQIuIhJQCWgpk0ZIM2pxyRrH0/fmUaZx7yd/jrju8TXtWrV4DwKndS/6MdPWaRM7fXvbb71zU59pdto1+7fn1V1ArV62mfddzOe60M7MvLkqUhYszaN/1XFocdxq9r+rHtm3b8my7/o8NHHLUCdnTwUJkStrD257CcR17cFzHHsyem/ctq8oKncVRQlUaf0NC+9t82uCE9hdrx44dlC9ftB+3D94ZlqBqUq9unb/w6rPxZ+QrTp9M+oImBzViyKP/LvA2mZmZpKWl5dtu4H0P8X9XXMLZ3U/n+v4DeWXY21x+ca+4be998DHaxbni9J7bbqZH104Frq20U0BLgWVmZnLNzbfz5VezqFtnX94Y+jSVKlVkwcLF3HTb3axcvZrKlSrx+AP30OSgA7iqX39q1qjO7G/nccThzeh5Rhf633kfm7dspVLFPXl68H00PvCAHPtYvWYNl/W9kZWr1nDUkYcTfR1VvSYtWfrjTD6fMo1Bg5+kVq2azPvhJ448/FCee+JBzIwJH33KgLsHsXetmhxxWDMWLl7Cmy//J8c+tmzZyg0D7mTWN9+SVr48993xD05o14bX3hzBuAkT2bx5M78sWkLXTh24558353of/v3IU7z34US2bNnKMUcdyWP/vjvXHTqWr1hJv/53snDxEgAG3z8wx9WPi5Zk8NfeVzH1ozFkZmZyx30P8fEnkzAzLjn/HP5+2UXZbTdv3sIFl19Nty4d6X3Bubnque3uQXw25UtqVK/Gi08PZp+9a8X9nmzZupWB9z7E5i1bOK5jDz54Zxjvjv+Qh5/4Dw50bH8id992U/Z73feK3nz06STuveMfLF7yK0NeeJXt27dzVIvmDL5vYI7Qdnc+mzyVoU8+BMD55/Tg/sFPxg3oWbO/ZfnKVXQ46ThmffNtrvW78tqbI3h3/EdkZmby3Q8/cU2fS9m2fTvD3h7NnhUq8NYr/6FWzRoMGfoKLw0bSfny5WnWrBnDhpXMX+4a4pACy2u60ev+cQcP3vNPPntvBP/65y3cMOCu7G3mL1jIO8Ne5N47+tP4oAN47+3/Mmn8SAbcdC13DXok1z4GDX6KtkcfxaTxI+nSsT1Lfl0at5bZc+cx6M4BfDnxXRYuXsLU6TPZsmUr1/cfyPBXn2P8yNdZuXp13G2fe/k1AL74aAwvPPUwV/W7lS1bIhMvzfluHi8+8whffDiakWPeI2PpslzbX9H7Aj55dzhTPxrDli1bef/Dibna3HLHvbRrezSTP3iHz94fwSFNGuf5vr742pssWpzB5+NHMuXD0Zxz5p9DSRs2buSvl17FOWd2jRvOGzdt4ojDDuXz90dwXJujGTT4SSD+96T5oU0ZcNM19DyjM5MmjGLtunUMvO9hxrz5MpPGj2TmN3MY+/6H2f02PaQxH499k1o1azBizDgmjHqdSRNGkZaWxpsjx+SoY/WatVSvVi37r6R6deuw7LfluerduXMn/7z733F/8QHc88CjHNuhG7feeT9bt8YfIvnu+594/smHmDj2Le554FEqVarEpPEjOeaoIxn29jsAPPL0c8yaNYvZs2czZMiQPN/7sNMRtBRYvOlGN2zcyJczZnHJlddnt9saNfbYo2un7COt9X/8wVXX9+fnhYswYPuO3LPhTZk2g1efewKA0045iRrVq8etpeWRzalfrw4Ahx/alMVLfqVKlco03C+dhvulA3B296689Nr/cm079cuZ9LnsQgCaHHQADdLrMX/BQgBObNeW6tUi828c3PhAlmQsJb1e3Rzbfz5lGo89M5TNmzezZu06DmlyEJ1PbZ+jzWeTp/KfYBghLS0tu894Pvl8CpdddF52uNWqWSN73fmX9eW6qy7n3J7xx//LlStHz26RaVXP7dmNi664Jt/vSZaZ33ybPT0rwLlnnsGUadPp2qkDaWlpdO8SmZL100lf8PWcuZx8+jkAbN6yhdrBNlniTRkRb2LB519+nVPbn5jrPQUY2P8G/rJvbbZt2851/7idR59+jn/065ur3fHHtqbqXntRdS+oVrUqnTucDESmiJ07LzJH+KGHHMwFF1xAjx496NGjR5xKSgYFtBRYvOlGd+50qlevxqQJo+JuU7lSpezH9z74GMcfewyvDX2SRUsy6HrOxXG3KcgNPfeMmro0rVw5dmTuKPB0oU7e7XL0m1aOHTG/RLZs2cqNt93NJ+OGk16vLvc//ETcaU8Lwz3v19z66JZ8MPEzzjmza8FudGqW7/fkz/3m/T5U3HPP7F+s7k6vs3tw56035tl+71o1Wbd+ffZnDUuX/UadOvvmavflV1/zxZdfMfSV19mwcRPbt2+nSpUq3DXgRur8JdJ+zz0rcMG5PXniPy/E3Vf096hcOcv+uSxXzrK/X2+98h++/vl3Ro8ezT333MPcuXOL/BlIKmiIQ4qkWtW92L9B/expOt2dOd99H7ft+vUbqFvnLwC8/ubIuG2Obd2Kt4I/nz/4+DPWrot/+6x4mhx4AAsXZ7BoSQYAI0aPy3Mfb46I7GP+gl/I+HUZjQ9sVKB9bAnCeO+aNdmwcSPvBMM8sU48ri1DX30DiIzdr/9jQ559tj+xHS+8Oiw7XFavWZu97rabrqVWzRo5ho2i7dy5M3ta1eGjxtL26JYF/p60atGcyVOns2r1GjIzMxn+zru0a3NM3NfyzrsTsqeBXb1mLYszfs3Rxsw4/tjW2bW8/tYounQ8JVdfzz/5EHO/nMicqR/zr9tv4byzunPXgEjw//b78ux63x3/EU0PbpLne7YrO3fuJGPpMk4++WQeeOAB1q5dy4YNeb//YaaAliJ77omHePWN4bQ7tTut23dl3PiP4ra77v/+xl2DBtOxRy8yd+6M26b/DX2ZPG06x3fqycefTaZB/YLP4FepUkUevu8OzrrwCk4783z2rb031eJMF3r5xeezc2cmbU85g95X3cDTg+/L8dfBrtSoXo1Lzj+Hth26cf7frqblEYfFbffvuwbw+ZRptD3lDE7sfBbf//hTnn1e0utsGtSvy7Gndqfdqd0ZPmpsjvWD7hrA1q1buf1fuWdYrFK5Mt//OJ8TOvfk08lTs4cECvI9qfOXfRnYvx9dz7mYdh17cMRhzTj9tNyhekiTg/jnLddx5vl/49gO3ehx/mX8/vuKXO3uGnATTz37Eke268jqNWu5+LyzAZj5zRyuvumfeb7+LJdfczNtTzmDth26sWr1Gm6+7sp8t4knMzOTPtfewuGHH06LFi3o168fNWrU2K2+Uk3TjeZB042WTBs2bmSvKlVwd2687W4ObLQ/fa/oneqyJAU03ahIyLz8+lu88dYotm3fTvNDm3LphX9NdUkiu00BLaVK3yt664hZSg2NQYuIhJQCuoRwd911WiRBSsr/JQV0CbFj/TLWbdxaYn6wRMLK3Vm1ahUVK1ZMdSn50hh0CfHH9DeAXqysVrdgFyyIlHEV18c/lROgYsWKpKenJ7Ga3ZOQgDazTsBjQBrwvLsPillvwfouwCagt7vPLMi2EuHbNrB+8nOpLkOkxOhwzyepLqHIijzEYWZpwFNAZ6AZ0MvMmsU06ww0Dr76AM8UYlsRkTIpEWPQxwDz3X2Bu28DhgHdY9p0B17xiKlADTOrW8BtRUTKpEQEdH1gSdTzjGBZQdoUZFsRkTIpEWPQ8T6xij3VIK82Bdk20oFZHyLDIwAbzOyHPOrZB1iZx7oCO7WoHUQkpJYECEsdEJ5awlIHhKeWsNQBiajlX7v1Yfr+RdpngiUioDOABlHP04HYWdbzalOhANsC4O7PAs/mV4yZzQjLtfRhqSUsdUB4aglLHRCeWsJSB4SrllRKxBDHdKCxmTUyswrAecDomDajgYstog2wzt2XFXBbEZEyqchH0O6+w8yuBsYTOVXuBXefa2ZXBuuHAOOInGI3n8hpdpfuatui1iQiUhok5Dxodx9HJISjlw2JeuxA7nvX5LFtEeU7DJJEYaklLHVAeGoJSx0QnlrCUgeEq5aUKZHzQYuIlAWai0NEJKRKVUCbWScz+8HM5ptZ/xTW8YKZLTezb1NVQ1BHAzObaGbzzGyumV2XojoqmtmXZvZNUEf8G+wlr540M5tlZmPzb12sdSw0szlm9rWZFe4WQYmvpYaZDTez74Ofl7YpqOHg4L3I+lpvZtcnu44wKTVDHMFl4z8SOYU5g8gZIr3c/bsU1HICsIHI1ZPxb1qXnDrqAnXdfaaZVQW+Anok+z0J5mKp4u4bzGwPYBJwXXBVadKZ2Q1AK6Cau3dNRQ1BHQuBVu6e8nOPzexl4HN3fz44o6qyu69NYT1pwK9Aa3dflKo6Uq00HUGH5rJxd/8MWJ2KfcfUsSxrUip3/wOYRwqu1Awu8c+6rfIewVdKjgzMLB04HXg+FfsPIzOrBpwADAVw922pDOfAKcDPZTmcoXQFtC4b3wUzawi0AKalaP9pZvY1sBz4wN1TUgfwKHALkPdclMnjwAQz+yq4UjZVDgBWAC8GQz/Pm1mVFNYDkWsi3khxDSlXmgK6wJeNlzVmthfwNnC9u69PRQ3ununuRxK5WvQYM0v60I+ZdQWWu/tXyd53Htq5e0siszn2DYbGUqE80BJ4xt1bABuBVH6GUwHoBryVqhrCojQFdEEuOS9zgjHft4HX3H1EqusJ/nT+BOiUgt23A7oFY7/DgPZm9t8U1AGAuy8N/l0OjCQyTJcKGUBG1F81w4kEdqp0Bma6++8prCEUSlNA67LxGMGHc0OBee4+OIV11DazGsHjSkAH4Ptk1+Hut7p7urs3JPLz8bG7X5jsOgDMrErwwS3BcEJHICVn/bj7b8ASMzs4WHQKkPQP16P0QsMbQCm65VWYLhs3szeAk4B9zCwDGOjuQ1NQSjvgImBOMP4LMCC4ejOZ6gIvB5/MlwPedPeUnuIWAn8BRga3LysPvO7u76ewnmuA14KDmwUE0zEkm5lVJnIm1t9Tsf+wKTWn2YmIlDalaYhDRKRUUUCLiISUAlpEJKQU0CIiIaWAFhEJKQW0iEhIKaAlpYJpLv8v6nk9MxteTPvqYWZ3JKCfh8ysfSJqEtkVnQctKRVM4jQ2GdOymtkUoFtRp/c0s/2B59y9Y2IqE4lPR9CSaoOAA4MJ2h80s4ZZNzows95mNsrMxpjZL2Z2tZndEMy4NtXMagXtDjSz94NZ4T43s0Nid2JmTYCtWeFsZi+Z2TPBDQ0WmNmJwY0W5pnZS0GbtKDdt8HE+v0Agikw9zazOsl5i6SsKjWXekuJ1R84LJjpLuuIOtphRKZJrUjkrvD/cPcWZvYIcDGR6UOfBa5095/MrDXwNBA7BNEOmBmzrGbQrhswJmhzOTDdzI4kMmVA/ayj+6z5RAIzg/Zv78ZrFikQBbSE3cTgZgN/mNk6IkEKMAdoHkyleizwVjCvBcCecfqpS2TO42hj3N3NbA7wu7vPATCzuUBD4FPgADN7AngXmBC17XKgXlFfnMiuKKAl7LZGPd4Z9XwnkZ/fcsDarCPwXdgMVM+j7+h+s/t29zVmdgRwGtAXOBe4LGhTMehTpNhoDFpS7Q+g6u5uHNyA4BczOwciU6wGoRprHnBQYfo2s32Acu7+NnA7OedIbkKKpgeVskMBLSnl7quAycEHcQ/uZjcXAH8zs2+AucS/F+VnQAuLGgcpgPrAJ8FUrS8Bt0L2TRAOAlJ6J24p/XSanZQZZvYYkXHnD4vYz5lAS3e/PTGVicSnI2gpS+4DKiegn/LAwwnoR2SXdAQtIhJSOoIWEQkpBbSISEgpoEVEQkoBLSISUgpoEZGQUkCLiISUAlpEJKQU0CIiIaWAFhEJKQW0iEhIKaBFREJKAS0iElL/Dy7EMvqAEVJUAAAAAElFTkSuQmCC",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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jPbZmaZZrY+VcWJiJRlsR8SPgn0An4CKgGXA08kuygREcl9mh3uPt/M0tw9k8g8zVNSUJeISJkXG9CbgkmLvjazB4h8cKgJ9EVEUiB2iOOiYNnVRCbPbwCcleyiREQk6gg6uPnrve5+IbAFuCtlVYmIyJ9H0MGYc+1giENERFIsdgx6ITA5uJpwY9ZCdx+czKJERCR3QC8NvsoRucxbRERSJEdAu7vGnUVEQiIRd/UWEZFioIAWEQmp2Lk4asU2MLNGyStHRESyxB5BjzGzallPzKwZuiehiEhKxAb0fURCei8zO4rITWMvTH5ZIiISexbHu2a2BzCByGl2Pdz9p5RUJiJSxmXd8uoJwKOWVwMWANeYGe5+bSqKExEpy7KOoGfELP8q2YWIiEhOWbe8ehnAzKoAW4J5ObImUNozdeWJiJRdsR8SfkTkTipZKgEfJq8cERHJEhvQFd19Q9aT4HHl5JYkIiKQO6A3mlnLrCfBqXabk1uSiIhA7tnsrgfeMrOlwfO6wF+TWpGIiAC5z4OebmaHAAcDBnzv7ttTUpmISBmXdR50e3f/2Mx6xqxvHJwHPSIFtYmIlGlZR9AnAh8DZ8Rp44ACWkQkybLOgx4Y/HtpassREZEsWUMcN+yqke5JKCKSfFlDHLr/oIhIyGQNcehehCIiIRN7R5WXzaxG1POaZvZCfp2YWScz+8HM5ptZ/zjrzcweD9bPjr4YJlifZmazzGxsEV6LiEipEnslYXN3X5v1xN3XAC121UEwodJTQGegGdAruBNLtM5A4+CrD/BMzPrrgHmFLV5EpDSLDehyZlYz60lwj8LYqw1jHQPMd/cF7r4NGAZ0j2nTHXjFI6YCNcysbrCPdOB04PkivA4RkVInNnwfBqaY2XAi5z+fC9ybTx/1gSVRzzOA1gVoUx9YBjwK3II+qBQRySHHEbS7vwKcBfwOrAB6uvur+fRhcZZ5QdqYWVdgubvne4MAM+tjZjPMbMaKFSvyay4iUuLlGr5w9++A7wrRRwbQIOp5OrC0gG3OBrqZWRegIlDNzP7r7rluVOvuzwLPArRq1Sr2F4CISKkTOwa9O6YTmbOjkZlVAM4DRse0GQ1cHJzN0QZY5+7L3P1Wd09394bBdh/HC2cRkbIovw8A8+XuO8zsamA8kAa84O5zzezKYP0QYBzQBZgPbAJ0SbmISD6KHNAA7j6OSAhHLxsS9diBvvn08QnwSSLqEREpDRIxxCEiIsVAAS0iElIKaBGRkFJAi4iElAJaRCSkFNAiIiGlgBYRCSkFtIhISCmgRURCSgEtIhJSCmgRkZBSQIuIhJQCWkQkpBTQIiIhpYAWEQkpBbSISEgpoEVEQkoBLSISUgpoEZGQUkCLiISUAlpEJKQU0CIiIaWAFhEJKQW0iEhIKaBFREJKAS0iElIKaBGRkFJAi4iElAJaRCSkFNAiIiGlgBYRCSkFtIhISCmgRURCSgEtIhJSCmgRkZBSQIuIhJQCWkQkpBTQIiIhpYAWEQmphAS0mXUysx/MbL6Z9Y+z3szs8WD9bDNrGSxvYGYTzWyemc01s+sSUY+ISGlQ5IA2szTgKaAz0AzoZWbNYpp1BhoHX32AZ4LlO4Ab3b0p0AboG2dbEZEyKRFH0McA8919gbtvA4YB3WPadAde8YipQA0zq+vuy9x9JoC7/wHMA+onoCYRkRIvEQFdH1gS9TyD3CGbbxszawi0AKYloCYRkRIvEQFtcZZ5YdqY2V7A28D17r4+7k7M+pjZDDObsWLFit0uVkSkpEhEQGcADaKepwNLC9rGzPYgEs6vufuIvHbi7s+6eyt3b1W7du0ElC0iEm6JCOjpQGMza2RmFYDzgNExbUYDFwdnc7QB1rn7MjMzYCgwz90HJ6AWEZFSo3xRO3D3HWZ2NTAeSANecPe5ZnZlsH4IMA7oAswHNgGXBpu3Ay4C5pjZ18GyAe4+rqh1iYiUdEUOaIAgUMfFLBsS9diBvnG2m0T88WkRkTJPVxKKiISUAlpEJKQU0CIiIaWAFhEJqYR8SCjFzyrsRdWje1G+Wl0iZyeKyK7Mmzcvz3UVK1YkPT2dPfbYI4kVFZ4CuoSoenQv6hxwGNUr76mAFimAavUPjrvc3Vm1ahUZGRk0atQoyVUVjoY4Sojy1eoqnEUSwMzYe++92bJlS6pLyZcCuoQwM4WzSIKUlP9LCmhJiqv69WfU2PeLpe/Tz76Imd/MybX8tTdHcNNtdwMw9NVhvDF8VLHsX6S4aAy6hGr+xPyE9jf7moMS2l8iuTvuTrlyu3888beLzktgRSLJoSNoKZBFSzI4+qQuXHPz7bRu35Ue51/G5s2RMbzZc+dxyhl/5dgO3bjgb1ezZu26uH1MmTaDU7ufR/NjO+Q4mn7smaGcdPrZHNuhG/c99HiO/d0w4C6O79STjKXL6HfrnZzY5Sxat++a3S7Wf//3Ni2PP40uZ13I1Omzspff//ATPD5kKBA54r7j3oc4+fRzaHn8aUyZNgOATZs3c8mV13Nsh270vqof7bueG/fIXCRZFNBSYD//sogrep/PtI/HUr1aNUaPmwDAldf9g7sG3MiUD0fT7JAm/PuRp+Ju/9vyFYwf+TpvvjSEO++PTF740aeT+PmXhUwc+xaTJozi6zlzmTx1OgA//fwLvc7qzqTxI9kvvT6333I9n457mykfvMPkqdP59rsfcvb/+3Luf/hJJox6g1FvvMAPP+X9V0Zm5g4mvvsWg+4cwKCg3udffp0a1asx5cPR3HLdVXw9Z26R3zORolBAS4Ht3yCd5oc2BeDIww9lccavrFv/B+vW/8FxbY8BoNc5PZgcHJHG6npaB8qVK8chTQ5ixcqVAHz82WQmfjaZ4087kxM69eTH+b/w8y+LAGiQXo+jjzoye/uRY9/n+E49Of60M5n343y+jwngGbNmc1zbY9hn71pUqFCBnmd0zvO1nNG5Y+R1ND+UxUt+BWDq9Jmc1a0LAM0OacKhTeOfpiWSLBqDlgLbc88K2Y/T0sqxZcuOQm1focKfFwW4//mg39V9uOzCnGPEi5ZkUKVy5eznCxdn8MSQF5j47lvUrFGdq/r1Z+vWrbn2UdAP57NqSUtLIzNzR1BK7I2ARFJLR9BSJNWrVY0MCwRHzf97+x2Oa3N0gbdvf+Jx/HfYCDZs3AjA0mW/s2Llqlzt/tiwgSqVK1G9WlWWr1jJBxM/z9WmVYvmTPpiOqvXrGH79u2Mend8oV5Lm6OPYmQwNv79j/P57vsfC7W9SKLpCFqK7JlHB9Gv/51s3ryZhvs34KmH7yvwtqeceBw//rSAU7tFjqCrVKnMs48/SFpazmOHw5sdQvPDmtK6fVca7teA1q1a5uqrzl/2pf8NfenQ7Tzq7FubIw5rRmZmZoFrufySXlx5fX+O7dCN5oc15dCmTahetWqBtxdJNCuJf9a1atXKZ8yIP86Zl4b93y1U++f3eLBQ7Yvb3l0GcuB+9VJdRqmWmZnJ9u07qFhxTxYsXEz383rz1WfvU6FChfw3ltDJ61LvLPPmzaNp06Y5lpnZV+7eqjjrKgwdQYsENm3eTNdzL2HH9h24O4Pvu1PhLCmlgBYJVN1rLz4d93aqyxDJpg8JRURCSgEtIhJSCmgRkZBSQIuIhJQCWgpk0ZIM2pxyRrH0/fmUaZx7yd/jrju8TXtWrV4DwKndS/6MdPWaRM7fXvbb71zU59pdto1+7fn1V1ArV62mfddzOe60M7MvLkqUhYszaN/1XFocdxq9r+rHtm3b8my7/o8NHHLUCdnTwUJkStrD257CcR17cFzHHsyem/ctq8oKncVRQlUaf0NC+9t82uCE9hdrx44dlC9ftB+3D94ZlqBqUq9unb/w6rPxZ+QrTp9M+oImBzViyKP/LvA2mZmZpKWl5dtu4H0P8X9XXMLZ3U/n+v4DeWXY21x+ca+4be998DHaxbni9J7bbqZH104Frq20U0BLgWVmZnLNzbfz5VezqFtnX94Y+jSVKlVkwcLF3HTb3axcvZrKlSrx+AP30OSgA7iqX39q1qjO7G/nccThzeh5Rhf633kfm7dspVLFPXl68H00PvCAHPtYvWYNl/W9kZWr1nDUkYcTfR1VvSYtWfrjTD6fMo1Bg5+kVq2azPvhJ448/FCee+JBzIwJH33KgLsHsXetmhxxWDMWLl7Cmy//J8c+tmzZyg0D7mTWN9+SVr48993xD05o14bX3hzBuAkT2bx5M78sWkLXTh24558353of/v3IU7z34US2bNnKMUcdyWP/vjvXHTqWr1hJv/53snDxEgAG3z8wx9WPi5Zk8NfeVzH1ozFkZmZyx30P8fEnkzAzLjn/HP5+2UXZbTdv3sIFl19Nty4d6X3Bubnque3uQXw25UtqVK/Gi08PZp+9a8X9nmzZupWB9z7E5i1bOK5jDz54Zxjvjv+Qh5/4Dw50bH8id992U/Z73feK3nz06STuveMfLF7yK0NeeJXt27dzVIvmDL5vYI7Qdnc+mzyVoU8+BMD55/Tg/sFPxg3oWbO/ZfnKVXQ46ThmffNtrvW78tqbI3h3/EdkZmby3Q8/cU2fS9m2fTvD3h7NnhUq8NYr/6FWzRoMGfoKLw0bSfny5WnWrBnDhpXMX+4a4pACy2u60ev+cQcP3vNPPntvBP/65y3cMOCu7G3mL1jIO8Ne5N47+tP4oAN47+3/Mmn8SAbcdC13DXok1z4GDX6KtkcfxaTxI+nSsT1Lfl0at5bZc+cx6M4BfDnxXRYuXsLU6TPZsmUr1/cfyPBXn2P8yNdZuXp13G2fe/k1AL74aAwvPPUwV/W7lS1bIhMvzfluHi8+8whffDiakWPeI2PpslzbX9H7Aj55dzhTPxrDli1bef/Dibna3HLHvbRrezSTP3iHz94fwSFNGuf5vr742pssWpzB5+NHMuXD0Zxz5p9DSRs2buSvl17FOWd2jRvOGzdt4ojDDuXz90dwXJujGTT4SSD+96T5oU0ZcNM19DyjM5MmjGLtunUMvO9hxrz5MpPGj2TmN3MY+/6H2f02PaQxH499k1o1azBizDgmjHqdSRNGkZaWxpsjx+SoY/WatVSvVi37r6R6deuw7LfluerduXMn/7z733F/8QHc88CjHNuhG7feeT9bt8YfIvnu+594/smHmDj2Le554FEqVarEpPEjOeaoIxn29jsAPPL0c8yaNYvZs2czZMiQPN/7sNMRtBRYvOlGN2zcyJczZnHJlddnt9saNfbYo2un7COt9X/8wVXX9+fnhYswYPuO3LPhTZk2g1efewKA0045iRrVq8etpeWRzalfrw4Ahx/alMVLfqVKlco03C+dhvulA3B296689Nr/cm079cuZ9LnsQgCaHHQADdLrMX/BQgBObNeW6tUi828c3PhAlmQsJb1e3Rzbfz5lGo89M5TNmzezZu06DmlyEJ1PbZ+jzWeTp/KfYBghLS0tu894Pvl8CpdddF52uNWqWSN73fmX9eW6qy7n3J7xx//LlStHz26RaVXP7dmNi664Jt/vSZaZ33ybPT0rwLlnnsGUadPp2qkDaWlpdO8SmZL100lf8PWcuZx8+jkAbN6yhdrBNlniTRkRb2LB519+nVPbn5jrPQUY2P8G/rJvbbZt2851/7idR59+jn/065ur3fHHtqbqXntRdS+oVrUqnTucDESmiJ07LzJH+KGHHMwFF1xAjx496NGjR5xKSgYFtBRYvOlGd+50qlevxqQJo+JuU7lSpezH9z74GMcfewyvDX2SRUsy6HrOxXG3KcgNPfeMmro0rVw5dmTuKPB0oU7e7XL0m1aOHTG/RLZs2cqNt93NJ+OGk16vLvc//ETcaU8Lwz3v19z66JZ8MPEzzjmza8FudGqW7/fkz/3m/T5U3HPP7F+s7k6vs3tw56035tl+71o1Wbd+ffZnDUuX/UadOvvmavflV1/zxZdfMfSV19mwcRPbt2+nSpUq3DXgRur8JdJ+zz0rcMG5PXniPy/E3Vf096hcOcv+uSxXzrK/X2+98h++/vl3Ro8ezT333MPcuXOL/BlIKmiIQ4qkWtW92L9B/expOt2dOd99H7ft+vUbqFvnLwC8/ubIuG2Obd2Kt4I/nz/4+DPWrot/+6x4mhx4AAsXZ7BoSQYAI0aPy3Mfb46I7GP+gl/I+HUZjQ9sVKB9bAnCeO+aNdmwcSPvBMM8sU48ri1DX30DiIzdr/9jQ559tj+xHS+8Oiw7XFavWZu97rabrqVWzRo5ho2i7dy5M3ta1eGjxtL26JYF/p60atGcyVOns2r1GjIzMxn+zru0a3NM3NfyzrsTsqeBXb1mLYszfs3Rxsw4/tjW2bW8/tYounQ8JVdfzz/5EHO/nMicqR/zr9tv4byzunPXgEjw//b78ux63x3/EU0PbpLne7YrO3fuJGPpMk4++WQeeOAB1q5dy4YNeb//YaaAliJ77omHePWN4bQ7tTut23dl3PiP4ra77v/+xl2DBtOxRy8yd+6M26b/DX2ZPG06x3fqycefTaZB/YLP4FepUkUevu8OzrrwCk4783z2rb031eJMF3r5xeezc2cmbU85g95X3cDTg+/L8dfBrtSoXo1Lzj+Hth26cf7frqblEYfFbffvuwbw+ZRptD3lDE7sfBbf//hTnn1e0utsGtSvy7Gndqfdqd0ZPmpsjvWD7hrA1q1buf1fuWdYrFK5Mt//OJ8TOvfk08lTs4cECvI9qfOXfRnYvx9dz7mYdh17cMRhzTj9tNyhekiTg/jnLddx5vl/49gO3ehx/mX8/vuKXO3uGnATTz37Eke268jqNWu5+LyzAZj5zRyuvumfeb7+LJdfczNtTzmDth26sWr1Gm6+7sp8t4knMzOTPtfewuGHH06LFi3o168fNWrU2K2+Uk3TjeZB042WTBs2bmSvKlVwd2687W4ObLQ/fa/oneqyJAU03ahIyLz8+lu88dYotm3fTvNDm3LphX9NdUkiu00BLaVK3yt664hZSg2NQYuIhJQCuoRwd911WiRBSsr/JQV0CbFj/TLWbdxaYn6wRMLK3Vm1ahUVK1ZMdSn50hh0CfHH9DeAXqysVrdgFyyIlHEV18c/lROgYsWKpKenJ7Ga3ZOQgDazTsBjQBrwvLsPillvwfouwCagt7vPLMi2EuHbNrB+8nOpLkOkxOhwzyepLqHIijzEYWZpwFNAZ6AZ0MvMmsU06ww0Dr76AM8UYlsRkTIpEWPQxwDz3X2Bu28DhgHdY9p0B17xiKlADTOrW8BtRUTKpEQEdH1gSdTzjGBZQdoUZFsRkTIpEWPQ8T6xij3VIK82Bdk20oFZHyLDIwAbzOyHPOrZB1iZx7oCO7WoHUQkpJYECEsdEJ5awlIHhKeWsNQBiajlX7v1Yfr+RdpngiUioDOABlHP04HYWdbzalOhANsC4O7PAs/mV4yZzQjLtfRhqSUsdUB4aglLHRCeWsJSB4SrllRKxBDHdKCxmTUyswrAecDomDajgYstog2wzt2XFXBbEZEyqchH0O6+w8yuBsYTOVXuBXefa2ZXBuuHAOOInGI3n8hpdpfuatui1iQiUhok5Dxodx9HJISjlw2JeuxA7nvX5LFtEeU7DJJEYaklLHVAeGoJSx0QnlrCUgeEq5aUKZHzQYuIlAWai0NEJKRKVUCbWScz+8HM5ptZ/xTW8YKZLTezb1NVQ1BHAzObaGbzzGyumV2XojoqmtmXZvZNUEf8G+wlr540M5tlZmPzb12sdSw0szlm9rWZFe4WQYmvpYaZDTez74Ofl7YpqOHg4L3I+lpvZtcnu44wKTVDHMFl4z8SOYU5g8gZIr3c/bsU1HICsIHI1ZPxb1qXnDrqAnXdfaaZVQW+Anok+z0J5mKp4u4bzGwPYBJwXXBVadKZ2Q1AK6Cau3dNRQ1BHQuBVu6e8nOPzexl4HN3fz44o6qyu69NYT1pwK9Aa3dflKo6Uq00HUGH5rJxd/8MWJ2KfcfUsSxrUip3/wOYRwqu1Awu8c+6rfIewVdKjgzMLB04HXg+FfsPIzOrBpwADAVw922pDOfAKcDPZTmcoXQFtC4b3wUzawi0AKalaP9pZvY1sBz4wN1TUgfwKHALkPdclMnjwAQz+yq4UjZVDgBWAC8GQz/Pm1mVFNYDkWsi3khxDSlXmgK6wJeNlzVmthfwNnC9u69PRQ3ununuRxK5WvQYM0v60I+ZdQWWu/tXyd53Htq5e0siszn2DYbGUqE80BJ4xt1bABuBVH6GUwHoBryVqhrCojQFdEEuOS9zgjHft4HX3H1EqusJ/nT+BOiUgt23A7oFY7/DgPZm9t8U1AGAuy8N/l0OjCQyTJcKGUBG1F81w4kEdqp0Bma6++8prCEUSlNA67LxGMGHc0OBee4+OIV11DazGsHjSkAH4Ptk1+Hut7p7urs3JPLz8bG7X5jsOgDMrErwwS3BcEJHICVn/bj7b8ASMzs4WHQKkPQP16P0QsMbQCm65VWYLhs3szeAk4B9zCwDGOjuQ1NQSjvgImBOMP4LMCC4ejOZ6gIvB5/MlwPedPeUnuIWAn8BRga3LysPvO7u76ewnmuA14KDmwUE0zEkm5lVJnIm1t9Tsf+wKTWn2YmIlDalaYhDRKRUUUCLiISUAlpEJKQU0CIiIaWAFhEJKQW0iEhIKaAlpYJpLv8v6nk9MxteTPvqYWZ3JKCfh8ysfSJqEtkVnQctKRVM4jQ2GdOymtkUoFtRp/c0s/2B59y9Y2IqE4lPR9CSaoOAA4MJ2h80s4ZZNzows95mNsrMxpjZL2Z2tZndEMy4NtXMagXtDjSz94NZ4T43s0Nid2JmTYCtWeFsZi+Z2TPBDQ0WmNmJwY0W5pnZS0GbtKDdt8HE+v0Agikw9zazOsl5i6SsKjWXekuJ1R84LJjpLuuIOtphRKZJrUjkrvD/cPcWZvYIcDGR6UOfBa5095/MrDXwNBA7BNEOmBmzrGbQrhswJmhzOTDdzI4kMmVA/ayj+6z5RAIzg/Zv78ZrFikQBbSE3cTgZgN/mNk6IkEKMAdoHkyleizwVjCvBcCecfqpS2TO42hj3N3NbA7wu7vPATCzuUBD4FPgADN7AngXmBC17XKgXlFfnMiuKKAl7LZGPd4Z9XwnkZ/fcsDarCPwXdgMVM+j7+h+s/t29zVmdgRwGtAXOBe4LGhTMehTpNhoDFpS7Q+g6u5uHNyA4BczOwciU6wGoRprHnBQYfo2s32Acu7+NnA7OedIbkKKpgeVskMBLSnl7quAycEHcQ/uZjcXAH8zs2+AucS/F+VnQAuLGgcpgPrAJ8FUrS8Bt0L2TRAOAlJ6J24p/XSanZQZZvYYkXHnD4vYz5lAS3e/PTGVicSnI2gpS+4DKiegn/LAwwnoR2SXdAQtIhJSOoIWEQkpBbSISEgpoEVEQkoBLSISUgpoEZGQUkCLiISUAlpEJKQU0CIiIaWAFhEJKQW0iEhIKaBFREJKAS0iElL/Dy7EMvqAEVJUAAAAAElFTkSuQmCC' 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       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6ebd432f49d4276acc9af4f8f87a0fd",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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sZj0GZmKeUxaDOzlHJAm5ml1OKAlrR5MQsxM7OlVT40tiN+JqGZWaqUSNqVzENjf1HsYszMbIky4F6gR0R8UuRazMwsSwnwGfBrSX9fXmNJXSR9JGmcpN451kvS35P170naJVm+taR3sr5mSzo/WXeZpC+y1h1Wy8doZlYnlQGP5tNQUilwK3AwUA6MkDQoIj7IatYVaJ987QHcBuwRER8BnbK28wUwMKvfjRFx3codiplZ/VIWEf2T0Fx7OW13B8ZFxHgASQOA7kB2QHcH7ouIAF6X1FRSy4iYktXmQODTiPi89g7DzKz+qbzMbh3gueW0bQVMynpfniwrtM1xwINVlp2TDIncLanZcqs2M1sDlABExBxg3eW0VY5lUUgbSQ2AI8hcNVLpNmBLMkMgU8g8Zbz6zqVekkZKGjl9+vTllGpmVvdVXge9KzB3OW3LyUzsX6k1MLnANl2BURExtXJBREyNiIqIWATcQWYopZqIuD0iOkdE5+bNmy+nVDOzuq9E0svAf4BzltN2BNBe0ubJmfBxwKAqbQYBJyVXc+wJfFNl/LknVYY3JLXMensk8P4KHIeZWb1TBvwf8GFELFhWw4hYKOkcYAhQCtwdEWMknZms7wcMBg4DxgHfA7+s7C9pXTJXgPyqyqavkdSJzFDIhBzrzczWSGUkl8VJIiIeW1bjiBhMJoSzl/XLeh3A2TX0/R7YMMfyE1egbjOzeq8MODx5HcAyA9rMzFafsoj45fKbmZnZ6uZnEpqZpZSfSWhmllJ+JqGZWUpV3qjSTNLdxS7GzMyWqLzVexawc5FrMTOzLJVn0BuQGY82M7OUKJF0BfAqcE2xizEzsyXKgOnAUVUm3jczsyIri4ibi12EmZlVV7L8JmZmVgwOaDOzlHJAm5mllAPazCylHNBmZinlgDYzSykHtJlZSjmgzcxSygFtZpZSDmgzs5RyQJuZpZQD2swspRzQZmYp5YA2M0spB7SZWUo5oM3MUqqggJbURdJHksZJ6p1jvST9PVn/nqRdstZNkDRa0juSRmYt30DSUEmfJN+brdwhmZnVD3kHtKRS4FagK9AR6CmpY5VmXYH2yVcv4LYq638SEZ0ionPWst7AsIhoDwxL3puZrfEKOYPeHRgXEeMj4gdgANC9SpvuwH2R8TrQVFLL5Wy3O9A/ed0f6FFATWZm9VYhAd0KmJT1vjxZlm+bAJ6V9JakXlltNomIKQDJ940LqMnMrN4qK6CtciyLAtrsExGTJW0MDJX0YUS8lPfOM6HeC6Bt27b5djMzq7MKOYMuB9pkvW8NTM63TURUfp8GDCQzZAIwtXIYJPk+LdfOI+L2iOgcEZ2bN29eQNlmZnVTIQE9AmgvaXNJDYDjgEFV2gwCTkqu5tgT+CYipkhqJKkxgKRGwCHA+1l9Tk5enww8sYLHYmZWr+Q9xBERCyWdAwwBSoG7I2KMpDOT9f2AwcBhwDjge+CXSfdNgIGSKvf5QEQ8k6zrCzwk6TRgInDMSh+VmVk9UMgYNBExmEwIZy/rl/U6gLNz9BsP7FTDNmcABxZSh5nZmsB3EpqZpZQD2swspRzQZmYp5YA2M0spB7SZWUo5oM3MUsoBbWaWUg5oM7OUckCbmaWUA9rMLKUc0GZmKeWANjNLKQe0mVlKOaDNzFLKAW1mllIOaDOzlHJAm5mllAPazCylHNBmZinlgDYzSykHtJlZSjmgzcxSygFtZpZSDmgzs5RyQJuZpZQD2swspRzQZmYp5YA2M0upggJaUhdJH0kaJ6l3jvWS9Pdk/XuSdkmWt5H0vKSxksZIOi+rz2WSvpD0TvJ12MoflplZ3VeWb0NJpcCtwMFAOTBC0qCI+CCrWVegffK1B3Bb8n0h8JuIGCWpMfCWpKFZfW+MiOtW/nDMzOqPvAMa2B0YFxHjASQNALoD2QHdHbgvIgJ4XVJTSS0jYgowBSAivpU0FmhVpa8tgxqsR+PdelLWpCWSil2OWeqNHTu2xnUNGzakdevWrLXWWquxosIVEtCtgElZ78vJnB0vr00rknAGkNQO2Bl4I6vdOZJOAkaSOdOeVUBda4TGu/WkxRbbs/66azugzfLQpNXWOZdHBDNmzKC8vJzNN998NVdVmELGoHOlQhTSRtJ6wKPA+RExO1l8G7Al0IlMkF+fc+dSL0kjJY2cPn16AWXXD2VNWjqczWqBJDbccEPmzZtX7FKWq5CALgfaZL1vDUzOt42ktciE8/0R8Vhlg4iYGhEVEbEIuIPMUEo1EXF7RHSOiM7NmzcvoOz6QZLD2ayW1JX/lgoJ6BFAe0mbS2oAHAcMqtJmEHBScjXHnsA3ETFFmZ/GXcDYiLghu4OklllvjwTeL/goLPXOuqA3jz/1zCrZ9k+PPpFR746utvz+hx7jokuuAOCufw3gwUceXyX7N1tV8h6DjoiFks4BhgClwN0RMUbSmcn6fsBg4DBgHPA98Muk+z7AicBoSe8ky/pExGDgGkmdyAyFTAB+tZLHtEbY8eZxtbq99369Va1urzZFBBFBScmKX7Z/2onH1WJFZqtHIR8SkgTq4CrL+mW9DuDsHP2Gk3t8mog4sZAarDg+n1TO0Sf2Ys/dduXNt96mZYuNefCuf7DOOg15b8xYLuh9GXPnzmXzzdpyy/VX0azp+tW28eobI7n1jnuZOv0rruhzET26dQHgptvuYuBT/+WH+T/QrctB9Lno3MX722/vPXjzrXd44K5buPHWOxj17mjmzZtP98MOoc9F51bbx7//8yg33HI7LTZuzpZbbM7aDTKf0v/l+ptp1Ghdzj3zNH569InsuvNOvPzqG3wzeza3XHcVe+/Rme/nzuWsC37PJ+PG06H9lkyc9AXXXfVHdtlph1X7wzWrge8ktLx9+tnnnHHK8bzxv6dYv0kTBg1+FoAzz/sdl/f5Da8+N4iO23TgrzfemrP/l9OmM2TgAzx0bz8u+0tmpGvYi8P59LMJPP/Uwwx/9nHeGT2GV14fAcAnn35Gz591Z/iQgbRt3Yo/Xnw+Lw5+lFeHPsErr4/g/Q8+Wnr7U6fxl+tv4dnHH+TxB+/mo09q/iujomIhzz/9MH0v60PfpN47+z9A0/Wb8Opzg7j4vLN4Z/SYlf6Zma0MB7TlbbM2rdlxu20B6LTDdkws/4JvZn/LN7O/Zd+9Mp/t9jymB6+8MTJn/26HHkRJSQnbdNiK6V99BcD/XnqF5196hf0OPZIfdTmKj8d9xqeffQ5Am9abstuunRb3H/jUM+zX5Sj2O/RIxn48jg+rBPDIt99j3712Z6MNN6BBgwYcdXjXGo/l8K6HZI5jx+2YOOkLAF4fMYqfHZG5kbXjNh3Ybtvcl2mZrS4FDXHYmm3ttRssfl1aWsK8eQsL6t+gwZKbAiKWvLjgnF6c+oulx4g/n1ROo3XXXfx+wsRybu53N88//TDNmq7PWRf0Zv78+dX2ke+H85W1lJaWUlGxMCml6lWjZsXlM2hbKes3aZwZFkjOmv/z6BPsu+duefc/YP99+feAx5jz3XcATJ4ylelfzajW7ts5c2i07jqs36Qx06Z/xdDnX67WpvPOOzL8tRHMnDWLBQsW8PjTQwo6lj1325WByZUmH348jg8+/Lig/ma1zWfQttJu+1vfxR8SttusDbdef3XefQ/cf18+/mQ8Bx+ROYNu1Ghdbv/7tZSWLn3usEPHbdhx+23Z44ButGvbhj0671JtWy022ZjeF57NQUccR4uNm7PT9h2pqKjIu5bTT+7Jmef3Zu+DjmDH7bdlu207sH7jxnn3N6ttqot/1nXu3DlGjsw9zlmTdr2fLqj9nWtdW1D7VW3Dwy5ly7abFruMeq2iooIFCxbSsOHajJ8wke7HncJbLz1DgwYNlt/ZUqemW70rjR07lm233XapZZLeiojOq7KuQvgM2izx/dy5dDv2ZBYuWEhEcMPVlzmcragc0GaJxuutx4uDHy12GWaL+UNCM7OUckCbmaWUA9rMLKUc0GZmKeWAtrx8PqmcPQ88fJVs++VX3+DYk3NPYrjDngcwY2bmATsHd6/7M9Jt2iFz/faUL6dyYq/qkz1lyz725W0vX1/NmMkB3Y5l30OPXHxzUW2ZMLGcA7ody877HsopZ13ADz/8UGPb2d/OYZtdf7R4OljITEm7w14Hsu8hPdj3kB68N6bmR1atKXwVRx21zpALa3V7cw+9YfmNVsLChQspK1u5f25DnxhQS9UUX8sWm/Cv2/++2vf7wvDX6LDV5vT721/z7lNRUUFpaely21169XX83xknc3T3n3J+70u5b8CjnH5Sz5xtr7r2JvbJccfplZf8dvEsh+aAtgJUVFTw69/+sdp0o+MnTOSiS67gq5kzWXeddfj7NVfSYastOOuC3jRruj7vvT+WnXboyFGHH0bvy65m7rz5rNNwbf5xw9W033KLpfYxc9YsTj37N3w1Yxa7dtqB7PuoNu2wC5M/HsXLr75B3xtuYYMNmjH2o0/otMN23HHztUji2WEv0ueKvmy4QTN22r4jEyZO4qH+/1xqH/PmzefCPpfx9rvvU1pWxtV/+h0/2mdP7n/oMQY/+zxz587ls88n0a3LQVz5h99W+zn89cZb+e9zzzNv3nx237UTN/31impP6Jg2/Ssu6H0ZEyZmHtF5w18uXerux88nlfPzU87i9WFPUlFRwZ+uvo7/vTAcSZx8/DH86tQls/DOnTuPE04/hyMOO4RTTji2Wj2XXNGXl159k6brN+Gef9zARhtukPN3Mm/+fC696jrmzpvHvof0YOgTA3h6yHNcf/M/CeCQA/bniksuWvyzPvuMUxj24nCu+tPvmDjpC/rd/S8WLFjArjvvyA1XX7pUaEcEL73yOnfdch0Axx/Tg7/ccEvOgH77vfeZ9tUMDvrxvrz9bmHP57j/ocd4esgwKioq+OCjT/h1r1/yw4IFDHh0EGs3aMDD9/2TDZo1pd9d93HvgIGUlZXRsWNHBgyom/9z9xCH5a2m6UbP+92fuPbKP/DSfx/jz3+4mAv7XL64z7jxE3hiwD1c9afetN9qC/776L8ZPmQgfS46l8v73lhtH31vuJW9dtuV4UMGctghBzDpi6pPVct4b8xY+l7Whzeff5oJEyfx+ohRzJs3n/N7X8oj/7qDIQMf4KuZM3P2vaP//QC8NuxJ7r71es664PfMm5eZeGn0B2O557Ybee25QQx88r+UT55Srf8Zp5zAC08/wuvDnmTevPk889zz1dpc/Ker2Gev3Xhl6BO89MxjbNOhfY0/13vuf4jPJ5bz8pCBvPrcII45cslQ0pzvvuPnvzyLY47sljOcv/v+e3bafjtefuYx9t1zN/recAuQ+3ey43bb0ueiX3PU4V0Z/uzjfP3NN1x69fU8+VB/hg8ZyKh3R/PUM88t3u6227Tnf089xAbNmvLYk4N59vEHGP7s45SWlvLQwCeXqmPmrK9Zv0mTxX8lbdqyBVO+nFat3kWLFvGHK/6a8398AFde8zf2PugIfn/ZX5g/P/cQyQcffsKdt1zH8089zJXX/I111lmH4UMGsvuunRjw6BMA3PiPO3j77bd577336NevX87t1AU+g7a85ZpudM533/HmyLc5+czzF7ebnzX22KNbl8VnWrO//Zazzu/NpxM+R8CChdVnw3v1jZH8646bATj0wB/TdP3qE/8D7NJpR1pt2gKAHbbblomTvqBRo3Vp17Y17dq2BuDo7t249/7/VOv7+puj6HXqLwDosNUWtGm9KePGTwBg/332Yv0mmfk3tm6/JZPKJ9N605ZL9X/51Te46ba7mDt3LrO+/oZtOmxF14MPWKrNS6+8zj+TYYTS0tLF28zlhZdf5dQTj1scbhs0a7p43fGnns15Z53OsUflHv8vKSnhqCMy06oee9QRnHjGr5f7O6k06t33F0/PCnDskYfz6hsj6NblIEpLS+l+WGZK1heHv8Y7o8fwk58eA8DcefNonvSplGvKiFwTC97Z/wEOPmD/aj9TgEt7X8gmGzfnhx8WcN7v/sjf/nEHv7ug2vM/2G/vPWi83no0Xg+aNG5M14N+AmSmiB0zNjNH+HbbbM0JJ5xAjx496NGjR45K6gYHtOUt13SjixYF66/fhOHPPp6zz7rrrLP49VXX3sR+e+/O/XfdwueTyul2zEk5++TzQM+1s6YuLS0pYWHFwrynC41qD6OvYbulJSys8j+RefPm85tLruCFwY/QetOW/OX6m3NOe1qIiJqPeY/ddmHo8y9xzJHd8nvQqbTc38mS/db8c2i49tqL/8caEfQ8ugeX/f43NbbfcINmfDN79uLPGiZP+ZIWLTau1u7Nt97htTff4q77HmDOd9+zYMECGjVqxOV9fkOLTTLt1167ASccexQ3//PunPvK/h2VlGjxv8uSEi3+fT183z9559OpDBo0iCuvvJIxY8as9GcgxeAhDlspTRqvx2ZtWi2epjMiGP3Bhznbzp49h5YtNgHggYcG5myz9x6deTj583no/17i62++ybuWDltuwYSJ5Xw+qRyAxwYNztlu7z0689BjmX2MG/8Z5V9Mof2Wm+e1j3lJGG/YrBlzvvuOJ5Jhnqr233cv7vrXg0Bm7H72t3Nq3OYB++/D3f8asDhcZs76evG6Sy46lw2aNV1q2CjbokWLFk+r+sjjT7HXbrvk/TvpvPOOvPL6CGbMnEVFRQWPPPE0++y5e85jeeLpZxdPAztz1tdMLP9iqTaS2G/vPRbX8sDDj3PYIQdW29adt1zHmDefZ/Tr/+PPf7yY437Wncv7ZIL/y6nTFtf79JBhbLt1hxp/ZsuyaNEiyidP4Sc/+QnXXHMNX3/9NXPm1PzzTzMHtK20O26+jn89+Aj7HNydPQ7oxuAhw3K2O+//TuPyvjdwSI+eVCxalLNN7wvP5pU3RrBfl6P430uv0KZV/jP4rbNOQ66/+k/87BdncOiRx7Nx8w1pkmO60NNPOp5FiyrY68DDOeWsC/nHDVcv9dfBsjRdvwknH38Mex10BMefdg677LR9znZ/vbwPL7/6BnsdeDj7d/0ZH378SY3bPLnn0bRp1ZK9D+7OPgd355HHn1pqfd/L+zB//nz++OfqMyw2WnddPvx4HD/qehQvvvL64iGBfH4nLTbZmEt7X0C3Y05in0N6sNP2HfnpodVDdZsOW/GHi8/jyONPY++DjqDH8acyder0au0u73MRt95+L532OYSZs77mpOOOBmDUu6M556I/1Hj8lU7/9W/Z68DD2eugI5gxcxa/Pe/M5fbJpaKigl7nXswOO+zAzjvvzAUXXEDTpk1XaFvF5ulGa+DpRuumOd99x3qNGhER/OaSK9hy8804+4xTil2WFYGnGzVLmf4PPMyDDz/ODwsWsON22/LLX/y82CWZrTAHtNUrZ59xis+Yrd7wGLSZWUo5oOuIiPBTp81qSV35b8kBXUcsnD2Fb76bX2f+YZmlVUQwY8YMGjZsWOxSlstj0HXEtyMeBHryVZOW+d2wYLaGazg796WcAA0bNqR169arsZoVU1BAS+oC3ASUAndGRN8q65WsPwz4HjglIkYtq6+kDYD/AO2ACcCxEbHsORbXQPHDHGa/ckexyzCrMw668oVil7DS8h7ikFQK3Ap0BToCPSV1rNKsK9A++eoF3JZH397AsIhoDwxL3puZrfEKGYPeHRgXEeMj4gdgANC9SpvuwH2R8TrQVFLL5fTtDvRPXvcHeqzYoZiZ1S+FBHQrYFLW+/JkWT5tltV3k4iYApB8rz7DipnZGqiQMehcn0xVvaSgpjb59F32zqVeZIZNAOZI+qiGphsBXxWy7VwOXtkNZNRKLbUgLXVAempJSx2QnlrSUgfURi1/XqEP0zdbqX3WskICuhxok/W+NVB1NvWa2jRYRt+pklpGxJRkOKT6LN9ARNwO3L68IiWNTMu99GmpJS11QHpqSUsdkJ5a0lIHpKuWYipkiGME0F7S5pIaAMcBg6q0GQScpIw9gW+SYYtl9R0EnJy8Phl4YgWPxcysXsn7DDoiFko6BxhC5lK5uyNijKQzk/X9gMFkLrEbR+Yyu18uq2+y6b7AQ5JOAyYCx9TKkZmZ1XEFXQcdEYPJhHD2sn5ZrwOo/oyaGvomy2cA1SehXXHLHQZZjdJSS1rqgPTUkpY6ID21pKUOSFctRVMn54M2M1sTeC4OM7OUqlcBLamLpI8kjZNUtDsSJd0taZqk94tVQ1JHG0nPSxoraYyk84pUR0NJb0p6N6kj9wP2Vl89pZLelvTU8luv0jomSBot6R1JhT0iqPZraSrpEUkfJv9e9ipCDVsnP4vKr9mSzl/ddaRJvRniSG4n/5jMJczlZK4c6RkRHxShlh8Bc8jcVZn7oXWrp46WQMuIGCWpMfAW0GN1/0ySOVoaRcQcSWsBw4HzkrtNVztJFwKdgSYR0a0YNSR1TAA6R0TRrz2W1B94OSLuTK60Wjcivi5iPaXAF8AeEfF5seootvp0Bp3PreirRUS8BMwsxr6r1DGlcrKqiPgWGEv1uz9XRx0REZWPVV4r+SrKmYGk1sBPgTuLsf80ktQE+BFwF0BE/FDMcE4cCHy6Jocz1K+AzudW9DWWpHbAzsAbRdp/qaR3yNyINDQiilIH8DfgYqDmuShXnwCelfRWcqdssWwBTAfuSYZ+7pTUqIj1QOZeiQeLXEPR1aeAXunbyesrSesBjwLnR8TsYtQQERUR0YnMXaS7S1rtQz+SugHTIuKt1b3vGuwTEbuQmeXx7GRorBjKgF2A2yJiZ+A7ijirZDLEcgTwcLFqSIv6FND53Iq+xknGfB8F7o+Ix4pdT/Kn8wtAlyLsfh/giGTsdwBwgKR/F6EOACJicvJ9GjCQzDBdMZQD5Vl/1TxCJrCLpSswKiKmFrGGVKhPAZ3PrehrlOTDubuAsRFxQxHraC6pafJ6HeAg4MPVXUdE/D4iWkdEOzL/Pv4XEb9Y3XUASGqUfHBLMpxwCFCUq34i4ktgkqStk0UHAqv9w/UsPfHwBlCPHnm1nNvJVytJDwI/BjaSVA5cGhF3FaGUfYATgdHJ+C9An+SuztWpJdA/+WS+BHgoIop6iVsKbAIMTB5fVgY8EBHPFLGeXwP3Jyc340mmaVjdJK1L5kqsXxVj/2lTby6zMzOrb+rTEIeZWb3igDYzSykHtJlZSjmgzcxSygFtZpZSDmgzs5RyQFtRJdNc/l/W+00lPbKK9tVD0p9qYTvXSTqgNmoyWxZfB21FlUzi9NTqmJZV0qvAESs7vaekzYA7IuKQ2qnMLDefQVux9QW2TCZov1ZSu8oHHUg6RdLjkp6U9JmkcyRdmMy49rqkDZJ2W0p6JpkV7mVJ21TdiaQOwPzKcJZ0r6TbkgcajJe0f/KghbGS7k3alCbt3k8m1r8AIJkCc0NJLVbPj8jWVPXmVm+rs3oD2ycz3VWeUWfbnsw0qQ3JPC3+dxGxs6QbgZPITB96O3BmRHwiaQ/gH0DVIYh9gFFVljVL2h0BPJm0OR0YIakTmSkDWlWe3VfOJ5IYlbR/dAWO2SwvDmhLu+eThw18K+kbMkEKMBrYMZlKdW/g4WReC4C1c2ynJZk5j7M9GREhaTQwNSJGA0gaA7QDXgS2kHQz8DTwbFbfacCmK3twZsvigLa0m5/1elHW+0Vk/v2WAF9XnoEvw1xg/Rq2nb3dxduOiFmSdgIOBc4GjgVOTdo0TLZptsp4DNqK7Vug8Yp2Th5A8JmkYyAzxWoSqlWNBbYqZNuSNgJKIuJR4I8sPUdyB4o0PaitORzQVlQRMQN4Jfkg7toV3MwJwGmS3gXGkPtZlC8BOytrHCQPrYAXkqla7wV+D4sfgrAVUNQncVv958vsbI0h6SYy487PreR2jgR2iYg/1k5lZrn5DNrWJFcD69bCdsqA62thO2bL5DNoM7OU8hm0mVlKOaDNzFLKAW1mllIOaDOzlHJAm5mllAPazCylHNBmZinlgDYzSykHtJlZSjmgzcxSygFtZpZSDmgzs5T6f40OOQwjMTDFAAAAAElFTkSuQmCC' width=360.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "\n",
    "time0=0.1\n",
    "time1=0.45\n",
    "dead_time=0.06\n",
    "    \n",
    "bins=np.arange(time1,7.6,time1-time0)#np.linspace(time0,7.6-time1,20)\n",
    "\n",
    "\n",
    "g2_0,  h_0, n_0, popt_0, bin_center_n, bin_center_h  = compute_g2_keep1stBar(click_0 ,time0,time1,dead_time, 'background')\n",
    "g2_pi, h_pi, n_pi, popt_pi, bin_center_n, bin_center_h = compute_g2_keep1stBar(click_pi,time0,time1,dead_time, 'pi pulse')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88dbc86d-9dfa-4005-a76e-0da974d947ab",
   "metadata": {},
   "source": [
    "## $g^2(\\tau)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02617052-a11f-48d7-9dee-e7cb45807959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['bin_center', 'bin_size', 'error_g2_0', 'error_g2_pi', 'g2_0', 'g2_list', 'g2_pi', 'g2corrected_list', 'h_list', 'n_list']>\n"
     ]
    }
   ],
   "source": [
    "# Load bootstrap data for ploting error bars\n",
    "with h5py.File(r'.\\g2_tau_bootstrap.hdf5', 'r') as f:\n",
    "    print(f.keys())\n",
    "    error_g2_0=f['error_g2_0'][()]\n",
    "    error_g2_pi=f['error_g2_pi'][()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "361ba8df-5686-4e69-84bc-7851c6afce6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.48819606 0.3702913  0.28543937 0.21094753 0.169575   0.13018304\n",
      " 0.09847654 0.08419072 0.05885587 0.05134368 0.04306771 0.04044387\n",
      " 0.03595572 0.03316424 0.02552938 0.02156291 0.02883975 0.01819058\n",
      " 0.0186901  0.01747182]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b64ad03861034da6a9fd8f349432b33d",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=504.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "bin_size=(bins[1:]-bins[:-1]).mean(0)\n",
    "bin_center=(bins[1:]+bins[:-1])/2\n",
    "\n",
    "plt.plot(bin_center_h,g2_0,'o' , label='background',color='black', ms=8)\n",
    "plt.plot(bin_center_h,g2_pi,'o' , label='pi pulse', color='red', ms=8)\n",
    "\n",
    "plt.errorbar(bin_center,g2_0,yerr=error_g2_0,fmt='none' ,color='black')\n",
    "plt.errorbar(bin_center,g2_pi,yerr=error_g2_pi,fmt='none' , color='red')\n",
    "\n",
    "n_0_m=n_0.mean()\n",
    "\n",
    "# Calculate g2 by using decay fit data\n",
    "n_pi_fit=exp_decay(bin_center_h,*popt_pi)\n",
    "T,a,n_0_m=popt_pi\n",
    "g2_single_emitter_with_DC=(n_0_m*(n_pi_fit[0]-n_0_m)+n_0_m*(n_pi_fit-n_0_m)+n_0_m*n_0_m)/(n_pi_fit[0]*n_pi_fit)\n",
    "\n",
    "plt.plot(bin_center_h,n_0[1:]/n_0[1:],'-',linewidth=2, color='black',  label='DC')\n",
    "plt.plot(bin_center_h,g2_single_emitter_with_DC,'-', label='single emitter with DC',linewidth=2,color='red')\n",
    "#plt.plot(bin_center,(n_0_m*(n_pi[0]-n_0_m)+n_0_m*(n_pi-n_0_m)+n_0_m*n_0_m)/(n_pi[0]*n_pi),'-', label='single emitter with DC',linewidth=2,color='red')\n",
    "\n",
    "\n",
    "SNR=(n_pi[1:]-n_0[1:])/n_0[1:]\n",
    "\n",
    "print(SNR)\n",
    "g2corrected=g2_pi[0]*(1+1/SNR[0])**2-2/SNR[0]-1/(SNR[0]**2)\n",
    "\n",
    "plt.ylabel('g2(tau)')\n",
    "plt.xlabel('tau (ms)')\n",
    "#plt.title(\"measured g2(0)=%.3f +/- %.3f \\n corrected g2(0)=%.3f +/- %.3f\"%(g2_pi[0], error_g2_pi[0], g2corrected, np.array(g2corrected_list).std()))\n",
    "plt.title(\"measured g2(0)=%.3f\\n corrected g2(0)=%.3f\"%(g2_pi[0], g2corrected))\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97a6690-cc43-4ee4-a999-f43bcd1a74bf",
   "metadata": {},
   "source": [
    "## Bootstrap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909159e0-54c5-4d01-a8c7-b5450f5637c4",
   "metadata": {},
   "source": [
    "Bootstrap to get error bars from the statistics\n",
    "\n",
    "If the file \"g2_tau_bootstrap.hdf5\" exists, we can skip this step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25b39537-d4d8-4cd4-8701-82d03bf6f2ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "time0=0.1\n",
    "time1=0.45\n",
    "\n",
    "bins=np.arange(time1,7.6,time1-time0)#np.linspace(time1,7.6-time1,20)\n",
    "\n",
    "\n",
    "h_list=[]\n",
    "n_list=[]\n",
    "g2_list=[]\n",
    "g2corrected_list=[]\n",
    "\n",
    "for i in range(100):\n",
    "    click_0_bootstrap=click_0[np.random.randint(0,len(click_0),len(click_0))]\n",
    "    click_pi_bootstrap=click_pi[np.random.randint(0,len(click_pi),len(click_pi))]\n",
    "\n",
    "    g2_0,  h_0, n_0  = compute_g2_noplot(click_0_bootstrap ,time0,time1,bins,'background')\n",
    "    g2_pi, h_pi, n_pi = compute_g2_noplot(click_pi_bootstrap ,time0,time1,bins,'pi pulse')\n",
    "\n",
    "    SNR=(n_pi-n_0)/n_0\n",
    "    g2corrected=g2_pi[0]*(1+1/SNR[0])**2-2/SNR[0]-1/(SNR[0]**2)\n",
    "    \n",
    "    \n",
    "    h_list.append([h_0,h_pi])\n",
    "    n_list.append([n_0,n_pi])\n",
    "    g2_list.append([g2_0,g2_pi])\n",
    "    g2corrected_list.append(g2corrected)\n",
    "    print(i)\n",
    "    print(g2_0)\n",
    "    print(g2_pi)\n",
    "    print(g2corrected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aed75c4-c438-456d-9d48-f60e81249dac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
